Fingerprint positioning method and system in smart classroom

Abstract

The disclosure discloses a fingerprint positioning method in a smart classroom, which is specifically: firstly, performing Gaussian filtering and taking the average value on a wireless signal strength value RSSI in the fingerprint database; then finding the neighbor point closest to the signal strength of the to-be-measured point; finally, the Euclidean distance is used as the weight reference, and the weighted center of mass is obtained for the nearest neighbor points. The weight index is introduced as an index of the weight, and the coordinates of the to-be-tested node are obtained. The disclosure has a higher positioning accuracy, smaller positioning error fluctuations and greater environmental adaptability.

Description

BACKGROUNDTechnical Field[0001]The disclosure belongs to the field of positioning technology, and more specifically relates to a fingerprint positioning method and system in a smart classroom.Description of Related Art[0002]In a smart classroom, it is important to carry out attendance check and take records, conduct real-time interaction between teachers and students, transmit teaching-related data and record and perform group-interaction based teaching, but these tasks are essentially based on the students' positions in the classroom space, so there is an urgent need to conduct research and studies on classroom space positioning. A variety of indoor positioning technologies have emerged for complex indoor environments, such as positioning technologies based on wireless local area networks, RFID, ZigBee and iBeacon. Indoor positioning based on iBeacon technology has the advantages of low cost and low power consumption, so many researches on indoor positioning algorithms based on iBe...

AI Technical Summary

Benefits of technology

The patent describes a method for improving the accuracy of positioning in a wireless network. It uses a technique called Gaussian filtering and averaging to reduce noise in the signal strength data. It then finds the closest neighbor to the signal strength of the new point and calculates the Euclidean distance between them. This distance is used as a weight to determine the center of mass of the neighbors. The weight index is introduced to measure the importance of each neighbor, and the coordinates of the new node are obtained. This method results in more accurate positioning, less positioning error fluctuation, and better environmental adaptability.

Problems solved by technology

With such algorithm, the environmental adaptability and positioning accuracy are improved, but the algorithms is more complicated.

Since the indoor environment is relatively complicated, the actual distance determined based on the signal attenuation model has large deviation; besides, the weight is fixed, there is no environmental adaptability, and the positioning accuracy is not high.

Although the positioning time is shortened, the accuracy is not improved and there is no environmental adaptability.

It can be learned that there is no research based on the Euclidean distance weight, and current researches have poor adaptability with respect to the complex indoor environment.

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